Thang D. Bui

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 19

Top Topics

Gaussian Process

Top Venues

J. Mach. Learn. Res.

Publications

Jack W. Miller, Charles O'Neill, Thang D. Bui
Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity. Trans. Mach. Learn. Res. 2024 (2024)
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR (2022)
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui
Variational Auto-Regressive Gaussian Processes for Continual Learning. ICML (2021)
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui
Variational Auto-Regressive Gaussian Processes for Continual Learning. CoRR (2020)
Theofanis Karaletsos, Thang D. Bui
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. CoRR (2020)
Theofanis Karaletsos, Thang D. Bui
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. NeurIPS (2020)
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner
Improving and Understanding Variational Continual Learning. CoRR (2019)
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
Variational Continual Learning. ICLR (Poster) (2018)
Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
Neural Graph Learning: Training Neural Networks Using Graphs. WSDM (2018)
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner
Partitioned Variational Inference: A unified framework encompassing federated and continual learning. CoRR (2018)
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
Variational Continual Learning. CoRR (2017)
Thang D. Bui, Josiah Yan, Richard E. Turner
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. J. Mach. Learn. Res. 18 (2017)
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
Streaming Sparse Gaussian Process Approximations. NIPS (2017)
Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
Neural Graph Machines: Learning Neural Networks Using Graphs. CoRR (2017)
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR (2016)
Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML (2016)
Thang D. Bui, Josiah Yan, Richard E. Turner
A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. CoRR (2016)
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
Black-Box Alpha Divergence Minimization. ICML (2016)
Felipe A. Tobar, Thang D. Bui, Richard E. Turner
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS (2015)
Thang D. Bui, Richard E. Turner
Tree-structured Gaussian Process Approximations. NIPS (2014)